Abstract
In the real world, people/entities usually find matches independently and autonomously, such as finding jobs, partners, roommates, etc. It is possible that this search for matches starts with no initial knowledge of the environment. We propose the use of a multi-agent reinforcement learning (MARL) paradigm for a spatially formulated decentralized two-sided matching market with independent and autonomous agents. Having autonomous agents acting independently makes our environment very dynamic and uncertain. Moreover, agents lack the knowledge of preferences of other agents and have to explore the environment and interact with other agents to discover their own preferences through noisy rewards. We think such a setting better approximates the real world and we study the usefulness of our MARL approach for it. Along with conventional stable matching case where agents have strictly ordered preferences, we check the applicability of our approach for stable matching with incomplete lists and ties. We investigate our results for stability, level of instability (for unstable results), and fairness. Our MARL approach mostly yields stable and fair outcomes.
Original language | English |
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Title of host publication | Algorithmic Decision Theory - 7th International Conference, ADT 2021, Proceedings |
Editors | Dimitris Fotakis, David Ríos Insua |
Pages | 375-389 |
Number of pages | 15 |
DOIs | |
State | Published - 2021 |
Event | 7th International Conference on Algorithmic Decision Theory, ADT 2021 - Toulouse, France Duration: Nov 3 2021 → Nov 5 2021 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 13023 LNAI |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 7th International Conference on Algorithmic Decision Theory, ADT 2021 |
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Country/Territory | France |
City | Toulouse |
Period | 11/3/21 → 11/5/21 |
Bibliographical note
Publisher Copyright:© 2021, Springer Nature Switzerland AG.
Keywords
- Decentralized system
- Multi-agent reinforcement learning
- Stable matching
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science